Detection of privacy breach during a communication session

ABSTRACT

A method of identifying a breach in privacy during a communication session, including communicating with a remote communication device using a local communication device, analyzing an audio signal from the remote communication device to identify an audio input/output configuration of the remote communication device, determining from the audio input/output configuration if a breach in privacy is signified.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. 120 from U.S.provisional application No. 62/346,607 dated Jun. 7, 2016, thedisclosure of which is incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to determination of audiostatus of a communication connection and more specifically todetermining if the communication connection is private or if there is abreach in the privacy of the connection.

BACKGROUND OF THE DISCLOSURE

Phone conversations and other audio conversations are held between twoor more participants located in different locations, sometimes indifferent countries and/or continents. The phone conversations may beheld between two or more telephone devices, such as mobile phones orlandline phones. The participants may share sensitive information duringthe conversation, such as personal data, financial data, legal data,confidential data, data regarding employment, security, safety and thelike.

In many cases, data is shared by a first participant based on thepresumption that the second participant receiving the information is thesole listener, although the audio status of the second participant maybe that he or she is speaking in speaker mode and is not alone, forexample while driving with other people in a vehicle. Such a scenarioresults in unwanted people listening to the sensitive data, as the firstparticipant is not aware of the mode of operation of the telephonedevice of the second participant.

Privacy breach of a conversation in this context can generally bedescribed as unaware exposure of the content of a conversation to aknown or unknown party due to ignorance of a participant as to whom islistening to the conversation. The privacy breach may be the result ofusing various peripheral equipment by the other participant, for examplewired speakers, built-in speakers, Bluetooth speakers, hands free carkits and other equipment allowing more than one person to listen to theconversation simultaneously. Likewise the privacy breach may be theresult of talking from a distance with the telephone device e.g. with aloud speaker (currently referred to as far talk in contrast to closetalk).

SUMMARY OF THE DISCLOSURE

An aspect of an embodiment of the disclosure, relates to a system andmethod for determining if a breach of privacy occurs during acommunication session between a local communication device and a remotecommunication device. An analysis application is installed on the localcommunication device to analyze audio signals from the remotecommunication device and determine an audio input/output configurationfrom the analysis. Identifying from the determined audio input/outputconfiguration if the communication session is secure or a breach inprivacy has occurred, for example since the remote communication deviceis conducted via a hands-free vehicle speaker system and not directlyinto the remote communication device.

In an exemplary embodiment of the disclosure, a monitoring applicationis installed on the remote communication device to monitor the audiooutput configuration of the remote communication device. Optionally, theremote communication device generates a message when an audioinput/output configuration change is detected and transmits the messagedirectly or indirectly to the local configuration device. The messagemay provide an indication that a security breach has developed so theuser of the local communication device will take precaution not todiscuss information, which is only intended privately for the user ofthe remote communication device.

In an exemplary embodiment of the disclosure, a combined application isused on communication devices, wherein the combined application monitorsthe audio output configuration of the device in which it is installedand analyzes audio signals from remote devices to determine their audiooutput configuration. The application may also accept messages fromremote communication devices regarding their audio input/outputconfiguration and may provide information about the audio outputconfiguration of the local communication device to remote devices withwhich it is communicating.

There is thus provided according to an exemplary embodiment of thedisclosure, a method of identifying a breach in privacy during acommunication session, comprising:

Communicating with a remote communication device using a localcommunication device;

Analyzing an audio signal from the remote communication device toidentify an audio input/output configuration of the remote communicationdevice;

Determining from the audio output configuration if a breach in privacyis signified.

In an exemplary embodiment of the disclosure, the method furthercomprises:

Receiving a message from the remote communication device indicating thata change has occurred in the audio input/output configuration of theremote configuration device;

Determining from the change if the audio input/output configuration ofthe remote communication device signifies a breach in privacy of thecommunication session.

Optionally, the message is provided by an application that monitors theaudio input/output configuration of the remote communication device. Inan exemplary embodiment of the disclosure, the determining from theaudio input/output configuration is compared to the determining from thechange to identify if the two are in agreement. Optionally, the messageis delivered directly to the local communication device. In an exemplaryembodiment of the disclosure, the message is delivered to a server todeliver to the local communication device. Optionally, the content ofthe received message is used to determine that a change has occurred inthe audio input/output configuration of the remote communication device.In an exemplary embodiment of the disclosure, the identity of a senderof the received message is used to determine that a change has occurredin the audio input/output configuration of the remote communicationdevice. Optionally, the audio input/output configuration indicates ifthe user of the remote communication device is speaking directly intothe remote communication device or speaking at a distance from theremote communication device. In an exemplary embodiment of thedisclosure, an indication is provided in real time to the user of thelocal communication device if a breach in privacy is signified.

There is further provided according to an exemplary embodiment of thedisclosure, a system for identifying a breach in privacy during acommunication session between a local communication device and a remotecommunication device, comprising:

An analysis application that is installable on the local communicationdevice, wherein the analysis application is programmed to analyze anaudio signal from the remote communication device to identify an audioinput/output configuration of the remote communication device anddetermine from the audio input/output configuration if a breach inprivacy is signified.

In an exemplary embodiment of the disclosure, the analysis applicationis further configured to:

Receive a message from the remote communication device indicating that achange has occurred in the audio input/output configuration of theremote configuration device;

Determine from the change if the audio input/output configuration of theremote communication device signifies a breach in privacy of thecommunication session. Optionally, the analysis application is furtherconfigured to be installed also on the remote communication device,monitor the audio input/output configuration of the remote communicationdevice and provide the message to the analysis application on the localcommunication device. In an exemplary embodiment of the disclosure, thedetermining from the audio input/output configuration is compared to thedetermining from the change to identify if the two are in agreement.Optionally, the message is delivered directly to the local communicationdevice. In an exemplary embodiment of the disclosure, the message isdelivered to a server to deliver to the local communication device.Optionally, the content of the received message is used to determinethat a change has occurred in the audio input/output configuration ofthe remote communication device. In an exemplary embodiment of thedisclosure, the identity of a sender of the received message is used todetermine that a change has occurred in the audio input/outputconfiguration of the remote communication device. Optionally, the audioinput/output configuration indicates if the user of the remotecommunication device is speaking directly into the remote communicationdevice or speaking at a distance from the remote communication device.In an exemplary embodiment of the disclosure, an indication is providedin real time to the user of the local communication device if a breachin privacy is signified.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood and better appreciated fromthe following detailed description taken in conjunction with thedrawings. Identical structures, elements or parts, which appear in morethan one figure, are generally labeled with the same or similar numberin all the figures in which they appear, wherein:

FIG. 1 is a schematic illustration of a communication environment,according to an exemplary embodiment of the disclosure;

FIG. 2 is a schematic illustration of components of a communicationdevice, according to an exemplary embodiment of the disclosure;

FIG. 3 is a flow diagram of a computerized method of transmitting anindication about connections to audio inputs/outputs of a communicationdevice, according to an exemplary embodiment of the disclosure;

FIG. 4 is a schematic illustration of an experiment setup for testingthe difference between close talk and far talk, according to anexemplary embodiment of the disclosure;

FIG. 5 is a graph of an LPC spectrum of an audio signal recorded inclose talk relative to far talk, according to an exemplary embodiment ofthe disclosure;

FIG. 6 is a graph of the measured signal power (dB) as a function oftime for close talk and far talk, according to an exemplary embodimentof the disclosure;

FIG. 7 is a graph of the measured signal in frequency domain for closetalk and far talk, according to an exemplary embodiment of thedisclosure.

FIG. 8 is a graph of a power spectrum for close talk and far talk infrequency domain, according to an exemplary embodiment of thedisclosure;

FIG. 9 is a graph of an LPC spectrum of an audio signal recorded inclose talk relative to far talk, according to an exemplary embodiment ofthe disclosure;

FIG. 10 is a graph depicting distribution results of estimated SNRvalues for close talk and far talk, according to an exemplary embodimentof the disclosure;

FIGS. 11a to 11i are histograms of a signal to noise and reverberationratio for various cases, according to an exemplary embodiment of thedisclosure;

FIG. 12 is a schematic diagram of an algorithm for determining if anaudio signal represents close talk or far talk in real time, accordingto an exemplary embodiment of the disclosure; and

FIG. 13 is a schematic diagram of a general structure of aclassification algorithm for processing the DFT domain samples from theanalysis of FIG. 12, according to an exemplary embodiment of thedisclosure.

DETAILED DESCRIPTION

The subject matter discloses a system and a method for providing anindication to a caller (or any participant) in an audio conversationthat the conversation with the other person or persons participating inthe call (e.g. the receiver) is exposed to unintended listeners in thevicinity of the receiver, so that the caller may refrain from exposinginformation that is only intended for the person or persons receivingthe call. In an exemplary embodiment of the disclosure, the method ofdetection is based on one or both of the following two types ofdetection:

1. By an agent application on the mobile device of the receiver of thecall to identify hardware that is used for sounding the conversation tothe receiver (listener).

2. By an analysis application at the mobile device of the caller thatanalyzes the audio signal received from the receiver to identify if thereceiver is speaking directly into the mobile device (close talk) orspeaking from a distance (far talk).

Optionally, other people can hear the conversation mainly when the audiotransmitted to the phone device is outputted in a non-standard way, forexample via a hands-free speaker in a vehicle. A non-standard way ofoutputting the audio is defined by any way of outputting the audio thatis not limited to the standard internal speaker of the phone device,which outputs the audio sounds directly to the user's ear. The subjectmatter of the disclosure provides for making a caller or any participantof a phone call aware of a privacy breach by other participants of theconversation, so that they may adapt a proper conversation behavior andrefrain from exposing private or confidential data to unwanted people orentities. When the caller or participant knows the status of audiooutputs of the other participants of the conversation, he/she can choosewhat they say accordingly. For example, when a participant in theconversation activates a loudspeaker (instead of a standard internalspeaker), the participant on the other side of the line can talk aboutthe weather and not about personal or confidential issues.

FIG. 1 is a schematic illustration of a communication environment 100,according to an exemplary embodiment of the disclosure. Thecommunication environment 100 includes a first participant 110 (e.g. acall receiver) with a communication device such as mobile device 115(e.g. a smartphone). The first participant 110 participates in a phoneconversation with a second participant 145 (e.g. a caller) that uses asecond communication device such as mobile device 150 or a landlinetelephone 160. The audio signals of the phone conversation aretransmitted via a network 130, such as a GSM network, the Internet, acellular network, a satellite communication network and the like. Duringthe phone conversation, the first person 110 may change the audioconnection of the mobile device 115. For example, the first participant110 may connect an earphone 120 to the mobile device 115, such thataudio signals from the second participant 145 are transmitted from themobile device 115 to the earphone 120. The earphone may be connected bywire or may be wireless (e.g. by Bluetooth). When the earphone 120 isconnected to the mobile device 115, a computerized application 125operating on the mobile device 115 detects the connection change to theaudio output and transmits a notification of the connection to a server140 (e.g. via the Internet or via the cellular network). The server 140then notifies the second participant 145 of the change at the firstparticipant 110. Optionally, the notification may be in the form of amessage (e.g. SMS message, WhatsApp message) or by calling the secondparticipant 145. Receipt of the message or call by the secondparticipant 145 from the server 140 (even without actually reading themessage or answering the call but just based on seeing the identity ofthe sender of the message or call (e.g. server 140)) provides a realtime indication that a privacy breach has occurred and that the secondparticipant 145 should be careful in what is disclosed to the firstparticipant 110. Alternatively, the second participant 145 may see themessage on a display of the mobile device 150 or landline device 160.The message can provide details of the event or just warn the secondparticipant 145 to beware since a possible privacy breach has occurred.

In an exemplary embodiment of the disclosure, the first participant 110may notify the second participant 145 directly, for example by sending amessage directly to the second participant 145 (e.g. the caller).Optionally, the message may state what was changed, for example that anearphone or loudspeaker was connected. Alternatively, the message may benull and the second participant 145 is notified by the identity of themessage sender.

In an exemplary embodiment of the disclosure, mobile device 150 orlandline telephone 160 include an application 155 for analyzing theaudio signal received from the first participant 110. Optionally,application 155 is programmed to analyze the audio signal and determineif the first participant 110 is speaking directly into the microphoneand listening to the standard internal speaker of the mobile device 115or if first participant 110 is using an external speaker, a carhand-free speaker system, an earphone 120 or other device (e.g. closetalk or far talk as explained above).

In an exemplary embodiment of the disclosure, applications 125 and 155are combined together into a single application that is installed in anycommunication devices such as mobile devices 115 and 150 or in alandline device 160. Optionally, this enables the communication device(115, 150, 160) to determine, which hardware is used in a conversation,notify other participants of the conversation and/or analyze aconversation to determine if a participant is speaking directly into themobile device or using external equipment or equipment that renders theconversation non-private. In some embodiments of the disclosure, theapplications (125, 150) can send messages directly from one another, forexample over a cellular network or over the Internet. Optionally, themessages can initiate an alarm including audio alerts (e.g. a bell orother sounds), visual alerts (e.g. flashing lights) or tactile alerts(e.g. vibrations) to notify a caller in real time that a privacy breachhas occurred at one of the participants in an ongoing conversation. Inan exemplary embodiment of the disclosure, all participants aresymmetric, thus during a conversation the first participant, the secondparticipant and any other participant (e.g. in a conference call) caneach provide notification (directly or via server 140) to all otherparticipants about hardware changes and can analyze the audio signal ofany other speaker.

In an exemplary embodiment of the disclosure, determination of a privacybreach may be based solely on analysis of the audio signal by the secondparticipant 145, for example if the mobile devices (115, 150) cannotreceive indications from application 125 regarding the hardware statusof mobile device 115. Alternatively, determination of a privacy breachmay be based solely on the detections of the first participant 110regarding the hardware used to sound the conversation to the firstparticipant (e.g. speaker, hands-free speaker, external speaker, andearphone). In some embodiments of the disclosure, determination may bemade based on agreement between analysis of the audio signal anddetection of the hardware status. Optionally, in case of disagreementapplication 125 will take precedence since it is based on hardwarestatus. Alternatively, application 155 will take precedence sincereports from participants may be unreliable (e.g. hacked to trick thesecond participant to disclose information).

FIG. 2 is a schematic illustration of components of a communicationdevice 200, according to an exemplary embodiment of the disclosure. Inan exemplary embodiment of the disclosure, each communication device 200(e.g. mobile device 115, 150 or landline device 160) may include thefollowing components discussed below:

A transceiver 240 configured to receive and transmit wirelesscommunication signals.

A display device 250 used to display data for the user of thecommunication device 200, for example data inputted by the user or dataregarding incoming calls, and the like.

An audio connections listener component 210 (e.g. a softwareapplication), which detects the audio connections used for input andoutput of audio from the communication device 200. Optionally, the audioconnections listener component 210 may detect the audio connections onlyduring phone calls conducted by communication device 200 or continuouslywhile the communication device 200 is activated. In an exemplaryembodiment of the disclosure, detection of audio connections may beperformed periodically, for example once or twice a second, or uponoccurrence of an event. For example, the operating system ofcommunication device 200 notifies the audio connections listener 210that changes were made with regard to the audio connections of thecommunication device 200.

In an exemplary embodiment of the disclosure, a list of the audioconnection configuration is stored in the communication device 200, forexample in an audio connection configuration memory 230. When there is achange in the list, for example connection of an external speaker to thecommunication device 200 or plugging in an audio input (e.g. externalmicrophone), a processor 260 of the communication device 200 notifiesthe audio connections listener 210. Optionally, changes to the audioconnections may comprise connection to or disconnection from an audioinput connection 265, or an audio output 270 of communication device200.

In an exemplary embodiment of the disclosure, a user of communicationdevice 200 may initiate a phone call to a second communication device200, while using only a standard internal speaker 275 of thecommunication device 200, located near the user's ear. During the phonecall, the user of the communication device 200 may change the operationmode of the communication device 200 into speaker mode for others tohear the conversation by providing the audio signals via a loud speaker280. Optionally, audio connections listener 210 detects the change tothe audio connection configuration and updates the audio connectionconfiguration memory 230.

In an exemplary embodiment of the disclosure, audio connections listener210 notifies a message generator 220 of the change in audio connections.For example, by raising a flag associated with use of audio inputconnections 265 or audio output connections 270, wherein an externaldevice is now connected or disconnected from the communication device200. The message generator 220 generates a message to be transmitted tothe server 140 by the transceiver 240. The server 140 sends the messagefrom the message generator 220 to another communication device 200 viaits transceiver 240 and the message is displayed on the display 250 ofthe other communication device 200. Optionally, the indication to theuser of the other communication device 200 may be provided by a textmessage, a vibration, by an audio message, by a graphic depiction, animage or other methods.

In an exemplary embodiment of the disclosure, the communication device200 also includes a storage 255 for storing notifications of changesmade by other communication devices 200 that conducted communicationsessions with communication device 200. For example, when thecommunication device 200 has a phone call log of the last 100 phonecalls, the user of the communication device 200 can review changes madeto the operation and of the audio input connections 265 and audio outputconnections 270 of the communication devices 200 that conductedcommunication sessions with communication device 200 in each of those100 phone calls. The storage 255 may store the type of audio inputconnections 265 and audio output connection 270, the change made, theduration of operation in each audio input connection 265 and audiooutput connection 270 and the like. For example, the storage 255 mayindicate for a specific phone call that a participant used an earphonefor the first 2.5 minutes, then used a loud speaker for 12 seconds, thenused the mute button for 7 seconds, and then an external speaker for therest of the conversation, The user may record the phone conversation andcompare the content of the conversation to the mode of operation of theaudio input connection 265 and/or audio output connection 270 and checkafter the conversation whether or not sensitive data was provided to theother participant when the other participant's phone was in speakermode.

In some embodiments of the disclosure, communication device 200 furtherincludes an analysis unit 285 that analyzes the audio signal receivedfrom other participants in a communication session to determine thecommunication status based on the audio signal received from theparticipant. Optionally, the results of the analysis are compared toinformation regarding the audio connection configuration provided by theparticipant who provided the audio signal to determine if there isagreement between the results of the analysis and the configurationinformation provided.

FIG. 3 shows a flow diagram of a computerized method 300 of transmittingan indication about audio output connections 270 of a communicationdevice 200, according to an exemplary embodiment of the disclosure.

Initially communication device 200 obtains (305) a list of audio outputconnections 270. The list of audio output connections 270 comprises alist of audio output connections 270, such as internal speaker 275, loudspeaker 280, earphone, hands-free device and the like. Additionally, thelist includes the status of each of the audio output connections 270(e.g. connected or disconnected, used or not used). For the purpose ofthis disclosure audio output connection 270 also includes pressing amute button or inputting a mute command to the mobile phone. The audiooutput connection 270 may also include recording the phone call.

In an exemplary embodiment of the disclosure, communication device 200detects (310) a change in the audio output connections 270. Such achange may occur when the user of the communication device 200 connectsor disconnects a device from an audio output connection 270, or decidesto turn on or turn off a device using an audio output connection 270.The change may also occur due to technical problems of an audio outputconnection 270, for example when an earphone fails to receive signalsfrom the device, the device automatically stops using the earphone. Thedetection of changes in the audio output connection 270 may be performedby an audio application listener 210 that is executed on thecommunication device 200. The audio application listener 210 monitors alist of audio output connections 270 or is embedded into the operatingsystem of the communication device 200 to detect (310) the change in theaudio output connection 270.

In an exemplary embodiment of the disclosure, upon detecting the changea message is generated (320) to be sent to a second communication device200, for example via the server 140. Optionally, when initiallyinstalling applications (e.g. 125, 155) on the communication devices200, the users of the communication devices 200 register at the server140, including the phone number and personal details of the user. Whendetecting a change in connections or operation of the audio outputs, themessage is sent to the proper communication device 200 by the server 140according to the data received during registration. Alternatively oradditionally, the information from the communication session or the opencommunication connection is used to notify the participants of thecommunication session.

In an exemplary embodiment of the disclosure, the message is transmitted(330) directly or indirectly (e.g. via server 140) to the othercommunication devices 200 participating in the communication session.Optionally, the message is accepted (340) by the other communicationdevices as an indication of change to audio output connections 270 ofthe transmitting communication device 200. The indication may beprovided as a textual message that is displayed on the display 250 ofthe other communication devices 200, a vibration on the othercommunication devices 200, a sound indication, for example text tospeech of the message sent and the like. Optionally, the indication isprovided to the users of the other communication devices in real time,during the phone call, such that the users of the other communicationdevices 200 knows whether or not it is safe to share sensitive data withthe users of the transmitting communication device 200.

In an exemplary embodiment of the disclosure, the message may provideinformation regarding the following parameters:

1. A pre-caller indication—notifying that a participant is starting touse a loudspeaker;

2. Bluetooth activation—notifying that a participant is activating aBluetooth device;

3. Silent Mode—notifying that a participant has muted his microphone;

4. RSSI—received signal strength indication to determine if theparticipant has strong enough reception;

5. Wi-Fi Status—notifying the status of Wi-Fi connections to theparticipant's device;

6. Conference Mode—notifying when adding or removing members of aconference call;

7. Secured Call—indicating that a call is secure;

8. File sharing—indicating if a participant is going to share a file(e.g. via WhatsApp);

9. Recording Indication—notifying if a participant is recording theconversation;

10. Battery indication—notifying if a participants battery is runninglow and there is a risk that the conversation may be disconnected.

In an exemplary embodiment of the disclosure, communication device 200stores (350) a log of the changes in connection and operation of audiooutputs during the communication session. For example, a user of acommunication device 200 has a log of phone calls with many users. Ineach phone call, the user may view if and when the other participantchanged the operation or connection of any audio output. For example, inan 8 minutes phone call, the log of changes may show that between thesecond and fifth minute, the phone device of the other participant wason speaker mode, and the last 45 seconds of the phone call was recordedby the other participant (e.g. by connecting a recording device).

In an exemplary embodiment of the disclosure, the difference between theaudio signal received over a cellular network from a user talkingdirectly into the communication device (close talk) and a user talkingat a remote position from the communication device (far talk) can bedetermined by experimentation.

FIG. 4 is a schematic illustration of an experiment setup 400 fortesting the difference between close talk and far talk, according to anexemplary embodiment of the disclosure. In setup 400 a computer 410 isused to provide sounds from a speaker 420. A mobile communication device430 is used to accept audio signals and transmit them to a remotecommunication device for analysis (e.g. a general purpose computer overnetwork 130 or e.g. to communication device 150 e.g. by application 155that is installed directly on the mobile communication device 150).

In some cases the mobile communication device 430 is placed in position440 near the speaker 420 (e.g. 1-5 cm from the speaker) to measure audiosignals in close talk and in some cases mobile communication device 430is placed in position 450 (e.g. 15-20 cm from the speaker) to measureaudio signals in far talk.

FIG. 5 is a graph 500 of an LPC spectrum of an audio signal recorded inclose talk 510 relative to far talk 520, according to an exemplaryembodiment of the disclosure. The general difference in the Linearpredictive Coding (LPC) between close talk and far talk is that forclose talk the LPC spectrum tends to generally decay, whereas for fartalk the LPC spectrum shows a peak before decaying and after decaying(two peaks), for example as shown in FIG. 5.

The audio signal transmitted by speaker 420 were initially chirpingsounds to make it easier to measure the acoustic transfer function (ATF)over the cellular network. FIG. 6 is a graph 600 of the measured signalpower (dB) as a function of time for close talk 610 and far talk 620.Likewise FIG. 7 is a graph 700 of the measured signal in frequencydomain for close talk 710 and far talk 720. In time domain it is evidentthat the far talk case 620 incorporates more reverberation (longerreverb) than in the close talk case 610. Likewise in frequency it can beseen that for the far talk case 720 the ATF is much more “peaky” withhuge differences between neighboring bands. Optionally, two bands thatare only a few Hz from each other can have a 20 dB difference in power.Moreover the “two hill pattern” that was seen in FIG. 5 can also berecognized in FIG. 7.

In an exemplary embodiment of the disclosure, the audio signal is madefrom a collection of voice signals, wherein some are of men and some areof women to check if it effects the results. Additionally, multiplelanguages can be used, for example English, Spanish, Chinese, Dutch,Hebrew, Arabic and even Finnish, to determine if there is an effect onthe results.

Likewise, 4 audio configurations were tested:

1. With the mobile communication device 430 in position 440 (closetalk);

2. With the mobile communication device 430 in position 440 and with theaudio signal from speaker 420 attenuated by 10 dB;

3. With the mobile communication device 430 in position 450 (Far talk)and with a box 460 between speaker 420 and the position (pos #3);

4. With the mobile communication device 430 in position 450 (Far talk)and without box 460 between speaker 420 and the position (pos #4).

Exemplary results of the experiments are depicted in FIG. 8 and FIG. 9.

FIG. 8 is a graph 800 of a power spectrum for close talk 810 and fartalk (820 (pos #3), 830 (pos #4)) in frequency domain; and FIG. 9 is agraph 900 of an LPC spectrum of an audio signal recorded in close talk910 relative to far talk (920 (pos #3), 930 (pos #4)) according to anexemplary embodiment of the disclosure.

It can be seen from the graphs (800, 900) that there is a significantdifference between an audio signal recorded in close talk and far talk,thus enabling a remote communication device 150 to determine the audiooutput configuration in the communication session that was conducted bycommunication device 430.

Optionally, Gaussian vector classification can be used to differentiatebetween close talk and far talk with an audio signal.

In an exemplary embodiment of the disclosure, when a far talk signaltravels through the cellular network, the mid-frequencies are stronglyattenuated, compared to the close talk signal. It is assumed that thequality of the far talk signal is low, and hence strongly manipulated bya speech enhancement unit (that is standardly provided on mobiledevices) on the device. What is left after this manipulation is mainlythe voiced speech main power (200-1200 Hz) and the unvoiced speech mainpower (2200-3500 Hz). The mid band (1200-2200 Hz) is strongly attenuatedbecause it is less important for intelligibility than the low and highbands.

Following is a mathematical formulation for the analysis describedabove. First, we denote the LPC-spectrum (LPCS) by S_(t)(k), where tdenotes the time-frame index, and k the frequency index in the shorttime Fourier transform (STFT) domain. Second, we describe the onlineestimation of S_(t)(k). Assume that S_(t−1)(k) was estimated at timeframe t−1 for every frequency band k. The LPCS is updated using therecursive formula(*)S _(t)(k)=λ·S _(t−1)(k)+(1−λ)·{tilde over (S)} _(t)(k),where 0<λ<1 is the smoothing coefficient, and {tilde over (S)}_(t)(k) isthe instantaneous LPC spectrum calculated by

${{(*}{*)}}\mspace{11mu}{{{{\overset{\sim}{S}}_{t}(k)} = \frac{\sigma_{w}^{2}\lbrack t\rbrack}{{{1 - {\sum\limits_{p = 1}^{P}\;{{a_{p}\lbrack t\rbrack} \cdot e^{\frac{2\;\pi\;{jkp}}{K}}}}}}^{2}}},}$and a_(l)[t], . . . , a_(P)[t], σ_(w) ²[t] are the LPC coefficientsestimated at the t-th time frame. The calculation of the LPC parametersis performed as commonly practiced in audio processing. We choose thevalue of the smoothing coefficient λ by the rule of thumb

${\lambda = {1 - \frac{1}{T_{smt}}}},$where T_(smt) is the number of time frames on which smoothing isexecuted. For example, if one decides to smooth over 13 time-frames, thecoefficient Δ=1−1/13≅0.92 will be used.Parametric Statistical Model

Define the following vector of the spectrum coefficients:S=[S _(t)(0),S _(t)(1), . . . , S _(t)(K− ¹)]^(T)where K is the number of frequency bands and is subject to tuning andtradeoff. For example, when long time-frames are used, K is larger andvice versa. However, zero-padding or frame folding can be used to reduceor enlarge K as needed. Therefore, the size of K should be determinedempirically, finding the size that gives the best performance. Guidinglines are—large K gives a better resolution, but also a high dimensionthat costs in computation and statistical perturbation. We attribute X aGaussian statistical model by the following probability density function(p.d.f.),

${{(*}{**)}}\mspace{11mu}{{{\left. {S_{t}(k)} \right.\sim{f\left( {S(k)} \right)}} = {\frac{1}{\sqrt{2{{\pi\sigma}_{s}^{2}(k)}}}{\exp\left( {- \frac{\left( {{S(k)} - {\mu_{s}(k)}} \right)^{2}}{\sigma_{s}^{2}(k)}} \right)}}},}$where μ_(s)(k) and σ_(s) ²(k) are the mean and variance of the Gaussianrandom variable X_(t)(k).Now, we learn μ_(s)(k) and σ_(s) ²(k) from the database, differently forclose talk and far talk, so we denote it by(****)μ_(c)(k),σ_(c) ²(k)μ_(f)(k),σ_(f) ²(k).

In summary, the training step is done by the following procedure:

-   -   1. New observations are measured: y_(t)(1), . . . , y_(t)(K).        Note: these are in the STFT domain.    -   2. The LPC parameters are calculated: a_(l)[t], . . . ,        a_(P)[t], σ_(w) ²[t].    -   3. The LPCS is calculated by (**), and smoothed by (*) to obtain        S_(t)(k).    -   4. Repeat 1-3 for far talk and close talk databases.    -   5. The mean and average values of S_(t)(k) are calculated for        the close talk and far talk, denoted as in (****) above.        Classification Algorithm

With the trained parameters in (****), a new frame can be classifiedusing a-posteriori probability of each hypothesis. The close talk andfar talk hypotheses are denoted by H_(e), H_(f), respectively. Furtherdenote the p.d.f.s regarding the close and far hypotheses byf_(f)(S_(t)(k)) and f_(c)(S_(t)(k)), respectively, where f_(f)(S_(t)(k))is obtained by substituting μ_(s)(k)←μ_(f)(k) and σ_(s) ²(k)←σ_(f) ²(k)in (***), and f_(c)(S_(t)(k)) is obtained similarly using μ_(c)(k),σ_(c)²(k). Now, the a-posteriori probabilities of close talk and far talkhypotheses are given by:

${p_{c}\left( {t,k} \right)} = {{P\left\{ H_{c} \middle| {S_{t}(k)} \right\}} = \frac{1}{1 + {\zeta\left( {t,k} \right)}}}$${p_{f}\left( {t,k} \right)} = {{P\left\{ H_{f} \middle| {S_{t}(k)} \right\}} = \frac{1}{1 + {\zeta^{- 1}\left( {t,k} \right)}}}$${\zeta\left( {t,k} \right)} = {\frac{f_{f}\left( {S_{t}(k)} \right)}{f_{c}\left( {S_{t}(k)} \right)}.}$

The online classification procedure is detailed below:

-   -   1. New observation is measured, and S_(t)(k) is calculated        identically to the training procedure.    -   2. Calculate the a-posteriori values        -   f_(f)(S_(t)(k)), f_(c)(S_(t)(k)), ζ(t,k), p_(c)(t,k),            p_(f)(t,k).    -   3. Integrate the a-posteriori probabilities by averaging in        frequency domain, and smoothing over time domain:

$\begin{matrix}{{p_{c}(t)} = {P\left\{ H_{c} \middle| \left\{ {{{{S_{\tau}(\kappa)}\text{:}\mspace{14mu}\tau} = {- \infty}},\ldots\mspace{11mu},t,{\kappa = 0},\ldots\mspace{11mu},{K - 1}} \right\} \right\}}} \\{{= {{\alpha \cdot {P_{c}\left( {t - 1} \right)}} + {\left( {1 - \alpha} \right) \cdot \left\lbrack {{1/K} \cdot {\sum\limits_{\kappa = 0}^{K - 1}\;{p_{c}\left( {\tau,\kappa} \right)}}} \right\rbrack}}},}\end{matrix}$where 0<α<1 is a constant time smoothing coefficient.

In an exemplary case the following values are calculated:

1) the LPCS of a close talk signal in STFT domain, 2) p_(c)(t,k), 3)p_(f)(t,k), and 4) P_(c)(t). Typically the values of P_(c)(t) are higherthan 50% when close talk speech is present.

In some embodiments of the disclosure, the support vector machine (SVM)approach can be used for classification, to determine if an audio signalis close talk or far talk.

In phone conversations, the transmitting device collects audio signalsusing a microphone. Except for the desired speech signal, inevitablenoise is also recorded. In far talk, the relative power of the speechsignal is lower, as compared to a close talk scenario, i.e., the SNR islower. Optionally, a spectral enhancement (SE) approach can be used toevaluate the SNR level, and use it to determine between close talk andfar talk.

In some embodiments of the disclosure, a variant of the log spectralamplitude (LSA) estimator can be used to estimate the SNR.

The algorithm steps are detailed below:

-   1. New observation is obtained, as in previous sections.-   2. The SNR of each STFT bin is estimated, using the LSA algorithm,    denoted by ξ(t,k).-   3. Calculate the average SNR of this time frame by ξ(t)=1/K·Σ_(κ=0)    ^(K−1)ξ(t,κ).-   4. An alert of far talk is raised in time frame t if ξ(τ)<ξ_(min)    for all τ=t−L, t−L+1, . . . , t. In words, if the SNR is lower than    ξ_(min) for a significant amount of time, i.e., L time-frames.    Note: the values of ξ_(min) and L are determined empirically by    using our database, as will be discussed in the following section.

In an exemplary embodiment of the disclosure, the performance of the SEmethod is evaluated, and the initial values of ξ_(min) and L that willbe used in the application are calculated. For that, we enlarged thedatabase as follows.

Using setup 400 as described above, numerous recordings were performedwith different values of SNR. The desired signal was taken from the ITUspeech database including speech with the following characteristics:

˜6 min long, 20 different speakers, 10 different languages, and thenoise was a babble noise. The SNR levels are 0 and 10 dB, differenttransmitting devices were used, and each recording was done twice: usingclose talk and far-talk setups. Likewise other noise environments mayalso be used, for example: machine noise, street noise, car noise, etc.Likewise real human speakers may be used instead of recordings.

It should be noted that the SNR used in the database creation with theSNR values estimated by the algorithm. When recording the database, theSNR is defined as the ratio between the desired and noise signals in thetransmitting room (the room where the transmitting device is located).In the database, the SNR is defined as the ratio between the SE-cleanedsignal and the SE-estimated noise at the recording device.

FIG. 10 is a graph 1000 depicting distribution results of estimated SNRvalues for close talk 1010 and far talk 1020.

In an exemplary embodiment of the disclosure, in each row, the SNR valuedistribution of a specific experiment is shown; for example, the firstrow shows the SNR distribution when the transmitting device is LG G2, inclose talk (dark) and far talk (light) scenarios. It seems thatξ_(min)=30 dB is a good separator between close talk and far talkscenarios.

As for the value of L, experiments show that using L=2/M fits, where Mis the number of time-frames per second, i.e., waiting for 2 secondswith low SNR to raise a far talk alert.

In an exemplary embodiment of the disclosure, as in the SNR evaluation,a spectral method for signal to noise and reverberation ratio (SNRR) isapplied. Then, using the enhanced signal, we calculate the SNRR as theratio between the enhanced signal and the attenuated interference(noise+reverb in this case). An overview of the algorithm is:

-   -   1. STFT—transformation of a frame of samples to the frequency        domain for processing.    -   2. Estimation of noise power spectral density (PSD), denoted by        ϕ_(n)(t,k). The estimator is based on the decision-directed (DD)        approach by Ephraim and Malah, and is slowly time-varying.    -   3. Estimation of the average PSD of the signal, denoted by        ϕ_(x)(t,k), simply by smoothing.    -   4. Estimation of the reverberant speech PSD, denoted by        ϕ_(r)(t,k). Direct path compensation (DPC) can be included or        not.    -   5. Estimation of the late reverberant speech PSD, denoted by        ϕ_(tr)(t,k).    -   6. Using the above PSDs, calculate the gain function using one        of several optimization criteria.    -   7. Apply a minimum gain constraint, and filter the signal in the        STFT.    -   8. Inverse Short Time Fourier Transformation (ISTFT).        SNR and SNRR Histogram Analysis

In an exemplary embodiment of the disclosure, when speaking in a car,without holding the device, but using either the device's speaker mode,or car speaking system. The car speaking system usually uses a BlueTooth(BT) connection to the device, and this speaking system can be built inby the car manufacturer, or installed by the customer. Using thespectral methods for denoising and dereverberation, it seems we have thebasis for classification. The (intuitive) assumption is that we canindicate the far talk in office by the low SNRR values, and far talk incar is characterized by low SNR values.

Histograms 1100 of SNRR in Office

In an exemplary embodiment of the disclosure, several signals wererecorded in an office (which was a small, but reverberant, room), theSNRR values in each STFT bin were measured. Afterwards, the distributionof the SNRR values was analyzed, and this is shown in FIGS. 11a to 11h .The first figure (FIG. 11a was calculated using the entire signal).

As expected, close-talk 1110 has higher SNRR values than far talk 1120,which can be used for classification. To emphasize the difference, weuse a VAD, and calculate the distributions in active frame, oralternatively, in non-active frames (see FIGS. 11b and 11c ).

Obviously the difference lies in the active parts of the signal. Theseresults encourage us to raise the VAD threshold. In the figure above, ifthe power of a frame is higher than the median power—we call it active,and vice versa. We might want to indicate speech only when the power ishigher than 90% of the frames, and expect to have even strongerclassification, as is shown in FIG. 11d and FIG. 11e ). Accordingly, theDRR values can be used for classification in a room.

Histograms of SNR in an Office

The office is relatively quiet environment, and the SNR evaluation isnot so different between close talk and far talk, as can be seen in FIG.11 f.

Moreover, it seems that far talk has lower noise, which is surelyuntrue. This result might be explained due to the processing in thedevice which changes the noise levels. This result can be separated tothe distribution of SNR values during active and non-active segments(see FIGS. 11g and 11h ).

Histograms of SNR in a Car

In the case of a car, when the speaker on the other side was using a BTspeaking system in the car. By comparing the distribution of the SNRvalues between car recordings 1140 and office recordings 1130 (see 1150in FIG. 11i ) the characteristics of such a case can be used. Thedifference in SNR distribution is very distinctive, and it can be usedfor classification.

In some embodiments of the disclosure, information about the personwhose speech is being analyzed can be taken into account to enhanceanalysis of the audio signal to classify the audio output configuration,for example by knowing the person's gender, accent, language, age andthe like.

In an exemplary embodiment of the disclosure, the main structure of thealgorithm 1200 for determining if an audio signal represents close talkor far talk in real time is perform as depicted in FIG. 12 showing thegeneral structure of frequency domain processing. Likewise it should benoted that there exists a general trade-off between false alarm (FA) andmiss detection (MD) when deciding if the audio signal is close talk orfar talk.

In an exemplary embodiment of the disclosure, at every time frame, D newaudio samples are streaming from the device, which are placed in abuffer of length K, where usually D≤K. Then, the buffer is multiplied bythe analysis window and transformed to the DFT domain.

A few notes are in place:

-   -   The synthesis (ISTFT) block is colored gray, since the        application will not necessarily produce an output signal.    -   This analysis-synthesis structure saves computation load in many        cases (linear filtering for example) but with the cost of        latency of K samples.    -   The increment size D is usually chosen such that the overlap        between two sequential buffers is 50% or 75%, which result in        smoother and more reliable results than 0% overlap. The higher        degree of overlap, the more computation per time unit is        required.    -   The exact size of D and K are determined to achieve the best        performance in the lowest computation.

In an exemplary embodiment of the disclosure, FIG. 13 demonstrates thegeneral structure of the classification algorithm 1300, processing theDFT domain samples from the analysis part,

The internal analysis blocks were discussed above—the Gaussian and SVM aposteriori analysis, and the SNR and DRR estimation. Here, we see thatthe algorithm integrates the information from these blocks and performsthe classification decisions.

As a concluding step, we combine the information received from thedifferent blocks by the following formula,p _(c)(t)=P{H _(c) |y(−∞), . . . , y(t)}=Pr{H _(c)|G(t),SVM(t),SNR(t),DRR(t)},Where G(t), SVM(t), SNR(t), DRR(t) are the Gaussian, SVM, SNR, and DRRestimates as computed in the previous section. The values SVR(t), DRR(t)are defined, and G(t), SVM(t) are merely the estimated probability ofhypothesis H_(c) obtained by the Gaussian and the SVM classifier. Toevaluate Pr{H_(c)| . . . }, we use empirical analysis over the recordeddatabase. Intuitively, Pr{H_(c)| . . . } gives a different weight foreach of the features to obtain the best classifying result.

For every algorithm that is design to detect and raise an alarm againstan event A, there is tradeoff between MD (an event A occurred withoutthe alarm set) and FA (an event A did not occur, but the alarm was setoff). A tradeoff always exists: to get MD=0 one can set the alarm alwayson (in this case, FA=1), and for FA=0, one can turn down the system andthe alarm will never work (in this case, MD=1). A good system reducesboth FA and MD, but when the optimal detection is obtained, one stillcan improve the cost function by a smart tradeoff between FA and MD.

This part of the algorithm is strongly connected to the user experience,but since undetected privacy breach can be very harmful, the choice isin the domain where FA>MD.

In some embodiments of the disclosure, additional enhancements may beused to improve the application, for example:

1. There is a challenge separating between the local and far speakers,since not all the devices allow for the recording of the local speakerseparated from the far source. Optionally it is desirable to get the AMRcoding data of the far speaker, which will save us the computation andtime to do this speech analysis ourselves. However, if such data isunavailable, we can divide the recording to segments that contain (ornot) the local speaker signal. The basis for separation is the [0, 300]Hz band, which is almost deleted by the AMR coder and therefore is builtmainly by the local speaker.2. In many cases, we use exponential time smoothing; e.g., assume wewant x_(n) to be a smoothed version of {tilde over (x)}_(n),x _(n−1) =α·x _(n)+(1−α)·{tilde over (x)} _(n+1),where 0≤α≤1 is a predefined smoothing factor. This method is very usefuland effective, but can be accelerated if we are in start-up mode. Inthis case, we replace the constant α by the time-varying α_(n):α_(n)=min{1−1/n,α _(max)},which has low values (i.e. fast convergence) in the beginning, growingmonotonically, and stabilized at 1−1/n>α_(max) to the value α_(max) tokeep updating in steady pace. Before it is stabilized, the value ofx_(n) is exactly the empirical average,

$x_{n} = {\frac{1}{n}{\sum\limits_{m = 1}^{n}\;{{\overset{\sim}{x}}_{m}.}}}$

A useful recursive formula for α_(n) is

$\alpha_{n + 1} = {{1 - \frac{1}{n + 1}} = {\frac{n}{n + 1} = {\frac{1}{1 + {1/n}} = {\frac{1}{2 - \left( {1 - {1/n}} \right)} = {\frac{1}{2 - \alpha_{n}}.}}}}}$

However, this isn't enough. In several cases, it is desirable to updatex_(n) depending on another value; e.g., if x_(n) is power estimation ofspeech, we would like to update it depending on the speech presenceprobability (SPP), so we don't add noise to the average. In this case,we'll use {tilde over (α)}_(n) instead,α_(n)=min{1−1/n,α _(max)}β_(n)=ζ_(n)·(1−α_(n)){tilde over (α)}_(n)=1−β_(n)=(1−ζ_(n))+ζ_(n)·α_(n),where 0≤ζ_(n)≤1 is the said weighting function.

It should be appreciated that the above described methods and apparatusmay be varied in many ways, including omitting or adding steps, changingthe order of steps and the type of devices used. It should beappreciated that different features may be combined in different ways.In particular, not all the features shown above in a particularembodiment are necessary in every embodiment of the disclosure. Furthercombinations of the above features are also considered to be within thescope of some embodiments of the disclosure.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather the scope of the present invention isdefined only by the claims, which follow.

We claim:
 1. A method of identifying a breach in privacy during acommunication session, comprising: communicating with a remotecommunication device using a local communication device; analyzing anaudio signal from the remote communication device to identify an audioinput/output configuration of the remote communication device;determining from the audio output configuration if a breach in privacyis signified; receiving a message from the remote communication deviceindicating that a change has occurred in the audio input/outputconfiguration of the remote configuration device; determining from thechange if the audio input/output configuration of the remotecommunication device signifies a breach in privacy of the communicationsession; wherein the determining from the audio input/outputconfiguration is compared to the determining from the change to identifyif the two are in agreement; and providing an indication if a breach inprivacy is detected.
 2. The method of claim 1, wherein the message isprovided by an application that monitors the audio input/outputconfiguration of the remote communication device.
 3. The method of claim1, wherein the message is delivered directly to the local communicationdevice.
 4. The method of claim 1, wherein the message is delivered to aserver to deliver to the local communication device.
 5. The method ofclaim 1, wherein the content of the received message is used todetermine that a change has occurred in the audio input/outputconfiguration of the remote communication device.
 6. The method of claim1, wherein the identity of a sender of the received message is used todetermine that a change has occurred in the audio input/outputconfiguration of the remote communication device.
 7. The method of claim1, wherein the audio input/output configuration indicates if the user ofthe remote communication device is speaking directly into the remotecommunication device or speaking at a distance from the remotecommunication device.
 8. The method of claim 1, wherein the indicationis provided in real time to the user of the local communication deviceif a breach in privacy is signified.
 9. The method of claim 1, whereinif the determining from the audio input/output configuration compared tothe determining from the change are not in agreement the determiningfrom the audio/input configuration will take precedence.
 10. The methodof claim 1, wherein if the determining from the audio input/outputconfiguration compared to the determining from the change are not inagreement the determining from the change will take precedence.
 11. Asystem for identifying a breach in privacy during a communicationsession between a local communication device and a remote communicationdevice, comprising: an analysis application that is installable on thelocal communication device wherein the analysis application isprogrammed to analyze an audio signal from the remote communicationdevice to identify an audio input/output configuration of the remotecommunication device and determine from the audio input/outputconfiguration if a breach in privacy is signified; wherein the analysisapplication is further configured to: receive a message from the remotecommunication device indicating that a change has occurred in the audioinput/output configuration of the remote configuration device; determinefrom the change if the audio input/output configuration of the remotecommunication device signifies a breach in privacy of the communicationsession; wherein the determining from the audio input/outputconfiguration is compared to the determining from the change to identifyif the two are in agreement; and an indication is provided if a breachin privacy is detected.
 12. The system of claim 11, wherein the analysisapplication is further configured to be installed also on the remotecommunication device, monitor the audio input/output configuration ofthe remote communication device and provide the message to the analysisapplication on the local communication device.
 13. The system of claim11, wherein the message is delivered directly to the local communicationdevice.
 14. The system of claim 11, wherein the message is delivered toa server to deliver to the local communication device.
 15. The system ofclaim 11, wherein the content of the received message is used todetermine that a change has occurred in the audio input/outputconfiguration of the remote communication device.
 16. The system ofclaim 11, wherein the identity of a sender of the received message isused to determine that a change has occurred in the audio input/outputconfiguration of the remote communication device.
 17. The system ofclaim 11, wherein the audio input/output configuration indicates if theuser of the remote communication device is speaking directly into theremote communication device or speaking at a distance from the remotecommunication device.
 18. The system of claim 11, wherein the indicationis provided in real time to the user of the local communication deviceif a breach in privacy is signified.
 19. The system of claim 11, whereinif the determining from the audio input/output configuration compared tothe determining from the change are not in agreement the determiningfrom the audio/input configuration will take precedence.
 20. The systemof claim 11, wherein if the determining from the audio input/outputconfiguration compared to the determining from the change are not inagreement the determining from the change will take precedence.